Estimating the term structure of commodity market preferences

George Christodoulakis

European Journal of Operational Research, 2020, vol. 282, issue 3, 1146-1163

Abstract: The commodity futures curve is viewed as a market-based path forecast, a term structure, optimizing multivariate loss preferences. Based on the forecast decision setting, we apply estimation of flexible multivariate loss functions, which reveal the preference term structure along the futures curve, which can be flat, smoothly sloping or oscillating, rotating among optimism, pessimism and symmetry. Evidence from the thirty main world commodities around the global crisis period, accommodates the futures curve forecast rationality questioned in the literature, suggesting the presence of joint preference asymmetries for longer maturities and symmetries for shorter ones. This reveals joint optimistic preferences for most commodities until 2004, evolving into oscillating preferences rotating within the term structure from symmetry to pessimism and optimism in 2005–2008 and finally back to weaker optimism until 2013.

Keywords: (B) Finance; Commodity futures; Forecast decision, Loss functions; Optimism; Preferences (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221719308410
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:282:y:2020:i:3:p:1146-1163

DOI: 10.1016/j.ejor.2019.10.009

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().